The present invention relates to X-ray detectors and, more particularly, to bad pixel identification in large area solid state X-ray detectors.
Large area solid state X-ray detectors have been developed in the X-ray art. Such detectors typically comprise a scintillating layer in contact with an array of photodiodes, organized in rows and columns, each with an associated FET switch. The scintillating layer converts X-ray photons to light photons. The array of photodiodes converts light photons to electrical intensity signals. The photodiodes are initially separately charged by connecting each of the photodiodes to a stable voltage source characterized by a known potential. Each photodiode is connected to the source via a dedicated FET switch (i.e., there is a separate FET switch for each of the photodiodes).
During operation, the photodiodes are isolated by turning off their FET switches. Upon exposure to X-rays, the scintillating layer produces light that discharges each photodiode in proportion to the X-ray exposure at the position of the diode. After a short exposure period, the diodes are recharged by reconnecting the diodes to the stable voltage source. The charge used to restore each diode to its initial voltage is measured by a sensing circuit, and the measured value is digitized and stored as an imaging array of digital intensity signals. After acquisition, the resulting intensity signal array comprises an X-ray image of the distribution of X-rays impinging on the detector. Hereinafter each photodiode-FET switch pair will be referred to as a detector “pixel”.
Solid state X-ray detectors of the type described above include a large number (e.g., several million) of detector pixels wherein each pixel generates a separate intensity signal. Because of non-uniformities in detector manufacturing processes, different regions of a detector are typically characterized by different readout behavior due to differences in the intrinsic characteristics and physical limitations of the detector. In order to produce diagnostic quality images, the differences in intrinsic characteristics and physical limitation must be compensated for. To this end, typical compensation algorithms often include subtracting a pixel specific offset value from each original uncorrected pixel intensity value and multiplying the result by a pixel specific gain correction factor.
The range of offset values and gain correction factors is limited and therefore, not surprisingly, at least some fraction of detector pixels generate signals that cannot be corrected to reflect actual X-ray intensity using the offset values and gain factors. These defective pixels are referred to hereinafter as “bad pixels”.
Fortunately, perfect detectors are not required to generate medical X-ray images. In this regard, the minimum size of objects that can be clearly seen in a medical image is determined by an imaging system detective quantum efficiency (DQE). For large area solid state detectors the factors affecting the detector's DQE include lateral spread of light photons and of secondary X-ray photons in the scintillating layer, the finite size and noise properties of the detector pixels. Thus, scintillating layer structure and pixel size can be designed so that a detector's DQE is adequate to generate images in which the smallest object of interest is observable. More specifically, to provide an adequate DQE, pixel size can be chosen so that even the smallest objects of interest to be imaged spread signal over more than one detector pixel (i.e., detector element). Where object signal is spread over more than one pixel, unless large numbers of “bad pixels” are aggregated in sizable clusters, the loss of information due to bad pixels is minimal.
Nevertheless, because signal intensity from bad pixels is either independent of X-ray exposure or depends on X-ray exposure in a way that is different than signal intensities generated by adjacent good pixels, the effects of bad pixels are visually noticeable as artifacts (e.g., pixels, lines etc) in resulting images and hence degrade diagnostic usefulness.
The industry has developed several ways in which to identify and replace bad pixels with suitable intensity values that substantially mitigate the effects of bad pixels. The process of compensating for bad pixels is generally a two step process including identifying bad pixels and then replacing the bad pixel intensities. With respect to identifying bad pixels, typically, prior to shipping detectors to customers, detector manufacturers perform tests on each manufactured detector to identify a “manufacturer's bad pixel map”. The manufacturer's map is provided to the customer along with the detector and is used by an image processor to correct for known bad pixels after image data is collected.
It is also known that detector elements have varying useful lives and that at least a percentage of detector pixels that are initially good when shipped by a manufacturer will become bad during detector use. For this reason, various tests have been devised to identify bad pixels in addition to the pixels that are included in the manufacturer's map and that can be added to the manufacturers map to provide a combined map.
One method for identifying additional bad pixels is to find each pixel that requires an offset value (defined as the signal obtained in the absence of X-ray exposure) or gain factor (defined as the signal obtained per unit of X-ray exposure) that lies outside an acceptable limit. Here, a pixel will be identified as bad if its offset value and/or gain factor lies outside a range that can be corrected with available readout electronics.
According to at least one method, an image of pixel offset values is created by averaging together several images obtained in the absence of X-ray exposure (sometimes referred to as “dark images”). Pixels whose offset values are either above the maximum correctable offset or below the minimum correctable offset value are identified as bad pixels.
An image of gain factors is created by averaging together several images obtained with uniform X-ray exposure, subtracting the offset value image, and normalizing to a value (typically the median). The gain factor image has pixel values that are proportional to corresponding pixel gains. Pixels whose gain factor are above a maximum correctable gain factor or below a minimum correctable gain factor are also identified as pixels with bad gain and also added to a bad pixel map.
These processes of identifying bad pixels from either offset, gain, or other tests can be performed at different times in the life of a detector. Performed during the detectors manufacturing is dubbed the “manufacturing bad pixel map”, performed on an installed product during a calibration is dubbed the “system bad pixel map”; performed on an installed product running as a background task when a system image processor detects that the imaging system is idle (i.e., when the detector is not in use), is a “run-time bad pixel map”.
A pixel may only be defective at certain X-ray energy ranges. For instance, assuming an X-ray source that can generate X-rays at five different energy levels, a detector pixel may be defective at one of the five levels and not at the other four. For this reason, at least some systems store several different energy dependent bad pixel maps and the appropriate bad pixel map is selected as a function of the X-ray energy level used during data acquisition.
During image processing, after pixel intensity data has been collected, for each bad pixel in the combined map, the image processor uses pixel intensity data corresponding to surrounding good pixels to generate a replacement pixel intensity value. Any of several different replacement intensity algorithms may be used to identify replacement pixel values such as weighted interpolation or extrapolation. One exemplary intensity determining algorithm is described in U.S. Pat. No. 5,657,400 which is entitled “Automatic Identification And Correction Of Bad Pixels In A Large Area Solid State X-ray Detector” which issued on Aug. 12, 1997 and which is commonly owned with the present invention.
While bad pixel correction processes clearly result in diagnostically more useful images, at some point the number and/or pattern of bad pixels exceeds minimum detector performance needed to ensure diagnostic quality images and the detector itself has to be repaired or replaced. Thus, for instance, when 10% of detector pixels are bad, the detector may be considered defective. As another instance, where a detectors detecting surface is dividable into one hundred separate sections including ten rows and ten columns, where 15% of the pixels in any one of the sections are bad, the detector may be considered defective.
Despite all of the detector tests described above, some bad pixels have been known to escape detection because at the time of the test, the pixel intensity values corresponding to these pixels pass the limits used to run the tests. In addition, despite meeting test requirements at the times the tests are run, some pixels operate differently during subsequent data acquisition processes. Bad pixels that are not included in the combined bad pixel map for any reason are referred to hereinafter as “maskable bad pixels”.
To compensate for maskable bad pixels it is known to provide an image to a system user via an interface display where the image has been corrected for each bad pixel in a manufacturer's bad pixel map. Thereafter, visually examining the corrected image via the display, the user is provided tools to select additional image pixels that the user believes to be bad. After the user selects all of the pixels that the user believes are bad, the user selected pixels are added to the manufacturer's bad pixel map and the updated map is then useable for subsequent correction processes.
While the above system and methods clearly increase the diagnostic usefulness of resulting-images, unfortunately the system and methods have several shortcomings. First, while maskable bad pixels do show up in images prior to replacement, it is tedious for a system user to identify each maskable bad pixel in an image for replacement. For instance, assume that 300 different maskable bad pixels are evident in an image when a system user observes the image. Independently selecting each of the 300 bad pixels either through specification of detector coordinates (e.g., row and column) or via a graphical user interface such as a mouse controlled cursor is extremely burdensome and, in many cases, will be foregone by the system user.
Second, even where a system user elects to earmark maskablebad pixels, the efficacy of earmarking will depend on how accurately the user perceives adjacent signal disparities. Thus, if a user's perception is slightly off, the user may earmark certain pixels as bad that in fact, by objective standards, would not be considered bad. The danger here is that a poorly perceiving user may cause the combined bad pixel map to be erroneously modified which would thereafter be used to degrade subsequent images generated by the system.
Third, it is known that different anatomical structures have different X-ray contrasts. For instance, cardiac X-ray images may be characterized by various vessel walls and adjacent fluids may be characterized by a first level of expected contrast (e.g., where disparate contrast between adjacent pixels is expected) while mammographic images may be characterized by vessels and fluids in which a second level of contrast is expected. Here, the threshold of visibility and contrast that should be applied when identifying maskable bad pixels should be different and should depend on the anatomical structure being imaged. In a system that relies on subjective standards applied by a system user to identify bad pixels, optimal thresholds most likely will not be applied.